{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "only use pandas calc nd, please install talib!\n",
      "/Users/Bailey/anaconda3/lib/python3.6/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The finance module has been deprecated in mpl 2.0 and will be removed in mpl 2.2. Please use the module mpl_finance instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n",
      "enable example env will only read RomDataBu/df_kl.h5\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import sys\n",
    "# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题\n",
    "sys.path.insert(0, os.path.abspath('../'))\n",
    "import abupy\n",
    "# 使用沙盒数据，目的是和书中一样的数据环境\n",
    "abupy.env.enable_example_env_ipython()\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set_context(rc={'figure.figsize': (14, 7) } )\n",
    "figzize_me = figsize =(14, 7)\n",
    "# import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.0 |Anaconda 4.3.1 (x86_64)| (default, Dec 23 2016, 13:19:00) \n",
      "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]\n"
     ]
    }
   ],
   "source": [
    "print(sys.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第3章 量化工具-Numpy\n",
    "\n",
    "[abu量化系统github地址](https://github.com/bbfamily/abu) (您的star是我的动力！)\n",
    "\n",
    "[abu量化文档教程ipython notebook](https://github.com/bbfamily/abu/tree/master/abupy_lecture)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 并行化思想与基础操作\n",
    "\n",
    "### 3.1.1  并行化思想"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 如下方式引用numpy是numpy.org推荐的方式                     \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 3.19 ms per loop\n"
     ]
    }
   ],
   "source": [
    "normal_list = range(10000)\n",
    "%timeit [i**2 for i in normal_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 16.42 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "100000 loops, best of 3: 6.49 µs per loop\n"
     ]
    }
   ],
   "source": [
    "np_list = np.arange(10000)\n",
    "%timeit np_list**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.  3.  3.  3.  3.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 注意 * 3的操作被运行在每一个元素上\n",
    "np_list = np.ones(5) * 3\n",
    "print(np_list)\n",
    "# 普通的列表把*3操作认为是整体性操作\n",
    "normal_list = [1, 1, 1, 1, 1] * 3\n",
    "normal_list, len(normal_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.2 初始化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 100个0\n",
    "np.zeros(100)\n",
    "# shape：3行2列 全是0\n",
    "np.zeros((3, 2))\n",
    "\n",
    "# shape： 3行2列 全是1\n",
    "np.ones((3, 2))\n",
    "# shape：x=2, y=3, z=3 值随机\n",
    "np.empty((2, 3, 3))\n",
    "\n",
    "# 初始化序列与np_list一样的shape，值全为1\n",
    "np.ones_like(np_list)\n",
    "# 初始化序列与np_list一样的shape，值全为0\n",
    "np.zeros_like(np_list)\n",
    "# eye得到对角线全为1的单位矩阵\n",
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[1, 2, 3, 4], [5, 6, 7, 8]]\n",
    "arr_np = np.array(data)\n",
    "arr_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.11111111,  0.22222222,  0.33333333,  0.44444444,\n",
       "        0.55555556,  0.66666667,  0.77777778,  0.88888889,  1.        ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 1, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 504)\n",
      "[[ 0.38035486  0.12259674 -0.2851901  -0.00889681  0.45731945]]\n"
     ]
    }
   ],
   "source": [
    "# 200支股票\n",
    "stock_cnt = 200\n",
    "# 504个交易日\n",
    "view_days = 504\n",
    "# 生成服从正态分布：均值期望＝0，标准差＝1的序列\n",
    "stock_day_change = np.random.standard_normal((stock_cnt, view_days))\n",
    "# 使用沙盒数据，目的是和书中一样的数据环境，不需要注视掉\n",
    "stock_day_change = np.load('../gen/stock_day_change.npy')\n",
    "# 打印shape (200, 504) 200行504列\n",
    "print(stock_day_change.shape)\n",
    "# 打印出第一支只股票，头五个交易日的涨跌幅情况\n",
    "print(stock_day_change[0:1, :5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.3 索引选取和切片选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.38035486,  0.12259674, -0.2851901 , -0.00889681,  0.45731945],\n",
       "       [ 0.13380956, -0.49312626,  1.44701057, -1.03491806,  0.42295542]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0:2第一，第二支股票，0:5头五个交易日的涨跌幅数据\n",
    "stock_day_change[0:2, 0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21652192, -0.03053515, -0.77747062, -1.19236603, -0.04788549],\n",
       "       [-0.96380496,  2.03488293,  0.99338065, -0.92392477,  0.96930104]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -2:倒数一，第二支股票，-5:最后五个交易日的涨跌幅数据\n",
    "stock_day_change[-2:, -5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.21652192, -0.03053515, -0.77747062, -1.19236603, -0.04788549],\n",
       "        [-0.96380496,  2.03488293,  0.99338065, -0.92392477,  0.96930104]]),\n",
       " array([[ 0.38035486,  0.12259674, -0.2851901 , -0.00889681,  0.45731945],\n",
       "        [ 0.13380956, -0.49312626,  1.44701057, -1.03491806,  0.42295542]]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tmp = a\n",
    "tmp = stock_day_change[0:2, 0:5].copy() \n",
    "# a = b\n",
    "stock_day_change[0:2, 0:5] = stock_day_change[-2:, -5:]\n",
    "# b = tmp\n",
    "stock_day_change[-2:, -5:] = tmp\n",
    "# view result\n",
    "stock_day_change[0:2, 0:5], stock_day_change[-2:, -5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.4 数据转换与规整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.21652192 -0.03053515 -0.77747062 -1.19236603 -0.04788549]\n",
      " [-0.96380496  2.03488293  0.99338065 -0.92392477  0.96930104]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0, -1,  0],\n",
       "       [ 0,  2,  0,  0,  0]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(stock_day_change[0:2, 0:5])\n",
    "stock_day_change[0:2, 0:5].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.22, -0.03, -0.78, -1.19, -0.05],\n",
       "       [-0.96,  2.03,  0.99, -0.92,  0.97]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2代表保留两位小数\n",
    "np.around(stock_day_change[0:2, 0:5], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[        nan, -0.03053515, -0.77747062, -1.19236603, -0.04788549],\n",
       "       [-0.96380496,  2.03488293,  0.99338065, -0.92392477,  0.96930104]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用copy目的是不修改原始序列\n",
    "tmp_test = stock_day_change[0:2, 0:5].copy()\n",
    "# 将第一个元素改成nan\n",
    "tmp_test[0][0] = np.nan\n",
    "tmp_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        , -0.03053515, -0.77747062, -1.19236603, -0.04788549],\n",
       "       [-0.96380496,  2.03488293,  0.99338065, -0.92392477,  0.96930104]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_test = np.nan_to_num(tmp_test)\n",
    "tmp_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.5 逻辑条件进行数据筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False False False False False]\n",
      " [False  True  True False  True]]\n"
     ]
    }
   ],
   "source": [
    "# 找出上述切片内涨幅超过0.5的股票时段, 通过输出结果你可以看到返回的是boolean的数组\n",
    "mask = stock_day_change[0:2, 0:5] > 0.5\n",
    "print(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.03488293,  0.99338065,  0.96930104])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_test = stock_day_change[0:2, 0:5].copy()\n",
    "# 使用上述的mask数组筛选出符合条件的数组, 即中筛选mask中对应index值为True的\n",
    "tmp_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21652192, -0.03053515, -0.77747062, -1.19236603, -0.04788549],\n",
       "       [-0.96380496,  1.        ,  1.        , -0.92392477,  1.        ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_test[tmp_test > 0.5] = 1\n",
    "tmp_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.38035486  0.12259674 -0.2851901  -0.00889681  0.45731945]\n",
      " [ 0.13380956 -0.49312626  1.44701057 -1.03491806  0.42295542]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1.44701057, -1.03491806])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_test = stock_day_change[-2:, -5:]\n",
    "print(tmp_test)\n",
    "tmp_test[(tmp_test > 1) | (tmp_test < -1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.6 通用序列函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.all判断序列中的所有元素是否全部是true, 即对bool序列进行与操作\n",
    "# 本例实际判断stock_day_change[0:2, 0:5]中是否全是上涨的\n",
    "np.all(stock_day_change[0:2, 0:5] > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.any判断序列中是否有元素为true, 即对bool序列进行或操作\n",
    "# 本例实际判断stock_day_change[0:2, 0:5]中是至少有一个是上涨的\n",
    "np.any(stock_day_change[0:2, 0:5] > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.38035486,  0.12259674, -0.2851901 , -0.00889681,  0.45731945],\n",
       "       [ 0.13380956,  2.03488293,  1.44701057, -0.92392477,  0.96930104]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对两个序列对应的元素两两比较，maximum结果集取大,相对使用minimum为取小的结果集\n",
    "np.maximum(stock_day_change[0:2, 0:5], stock_day_change[-2:, -5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0  0 -1  0]\n",
      " [ 0  2  0  0  0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-1,  0,  2])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change_int = stock_day_change[0:2, 0:5].astype(int)\n",
    "print(change_int)\n",
    "# 序列中数值值唯一且不重复的值组成新的序列\n",
    "np.unique(change_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.24705707, -0.74693547, -0.41489541,  1.14448054],\n",
       "       [ 2.9986879 , -1.04150228, -1.91730542,  1.89322581]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axis＝1\n",
    "np.diff(stock_day_change[0:2, 0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.18032689,  2.06541808,  1.77085127,  0.26844126,  1.01718653]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 唯一区别 axis=0\n",
    "np.diff(stock_day_change[0:2, 0:5], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0]\n",
      " [0 0 1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "tmp_test = stock_day_change[-2:, -5:]\n",
    "print(np.where(tmp_test > 0.5, 1, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.38035486  0.12259674 -0.2851901  -0.00889681  0.45731945]\n",
      " [ 0.13380956 -0.49312626  1.         -1.03491806  0.42295542]]\n"
     ]
    }
   ],
   "source": [
    "print(np.where(tmp_test > 0.5, 1, tmp_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 序列中的值大于0.5并且小于1的赋值为1，否则赋值为0\n",
    "np.where(np.logical_and(tmp_test > 0.5, tmp_test < 1), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 1, 1, 0]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 序列中的值大于0.5或者小于－0.5的赋值为1，否则赋值为0\n",
    "np.where(np.logical_or(tmp_test > 0.5, tmp_test < -0.5), 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.7  数据本地序列化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 504)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_day_change = np.load('../gen/stock_day_change.npy')\n",
    "\n",
    "np.save('../gen/stock_day_change', stock_day_change)\n",
    "\n",
    "stock_day_change.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 统计概念与函数使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.1 统计基础函数使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.38035486,  0.12259674, -0.2851901 , -0.00889681],\n",
       "       [ 0.13380956, -0.49312626,  1.44701057, -1.03491806],\n",
       "       [ 1.49695798,  1.17420526,  0.26125628,  0.70377972],\n",
       "       [-1.57012465,  0.25266829,  1.14584289,  0.29308672]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_day_change_four = stock_day_change[:4, :4]\n",
    "stock_day_change_four"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最大涨幅 [ 0.38035486  1.44701057  1.49695798  1.14584289]\n"
     ]
    }
   ],
   "source": [
    "print('最大涨幅 {}'.format(np.max(stock_day_change_four, axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最大跌幅 [-0.2851901  -1.03491806  0.26125628 -1.57012465]\n",
      "振幅幅度 [ 0.23989905  0.92537522  0.46843241  0.99049224]\n",
      "平均涨跌 [ 0.05221617  0.01319395  0.90904981  0.03036831]\n"
     ]
    }
   ],
   "source": [
    "print('最大跌幅 {}'.format(np.min(stock_day_change_four, axis=1)))\n",
    "print('振幅幅度 {}'.format(np.std(stock_day_change_four, axis=1)))\n",
    "print('平均涨跌 {}'.format(np.mean(stock_day_change_four, axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最大涨幅 [ 1.49695798  1.17420526  1.44701057  0.70377972]\n"
     ]
    }
   ],
   "source": [
    "print('最大涨幅 {}'.format(np.max(stock_day_change_four, axis=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最大涨幅股票[2 2 1 2]\n"
     ]
    }
   ],
   "source": [
    "print('最大涨幅股票{}'.format(np.argmax(stock_day_change_four, axis=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最大跌幅股票[3 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "print('最大跌幅股票{}'.format(np.argmin(stock_day_change_four, axis=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最大跌幅 [-1.57012465 -0.49312626 -0.2851901  -1.03491806]\n",
      "振幅幅度 [ 1.09773969  0.59620404  0.69038879  0.64260976]\n",
      "平均涨跌 [ 0.11024944  0.26408601  0.64222991 -0.01173711]\n"
     ]
    }
   ],
   "source": [
    "print('最大跌幅 {}'.format(np.min(stock_day_change_four, axis=0)))\n",
    "print('振幅幅度 {}'.format(np.std(stock_day_change_four, axis=0)))\n",
    "print('平均涨跌 {}'.format(np.mean(stock_day_change_four, axis=0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.2  统计基础概念"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a_investor = np.random.normal(loc=100, scale=50, size=(100, 1))\n",
    "b_investor = np.random.normal(loc=100, scale=20, size=(100, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a交易者期望102.68元, 标准差45.20, 方差2042.83\n",
      "b交易者期望100.13元, 标准差18.62, 方差346.74\n"
     ]
    }
   ],
   "source": [
    "# a交易者\n",
    "print('a交易者期望{0:.2f}元, 标准差{1:.2f}, 方差{2:.2f}'.format(\n",
    "    a_investor.mean(), a_investor.std(), a_investor.var()))\n",
    "\n",
    "# b交易者\n",
    "print('b交易者期望{0:.2f}元, 标准差{1:.2f}, 方差{2:.2f}'.format(\n",
    "    b_investor.mean(), b_investor.std(), b_investor.var()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x11a1b17f0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+HMBPADAB/HK32/3TXI74iLDf07HSrnk7hWHUZCkzZaYMAQBqiDIDYCabhK5z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d4d0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a交易者期望\n",
    "a_mean = a_investor.mean()\n",
    "# a交易者标注差\n",
    "a_std = a_investor.std()\n",
    "# 收益绘制曲线\n",
    "plt.plot(a_investor)\n",
    "# 水平直线 上线\n",
    "plt.axhline(a_mean + a_std, color='r')\n",
    "# 水平直线 均值期望线\n",
    "plt.axhline(a_mean, color='y')\n",
    "# 水平直线 下线\n",
    "plt.axhline(a_mean - a_std, color='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x11a5b5b70>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3z5kBgDlbmSGbmYti23UK+Vzga9iznmxmxLhhmxQKFTOzDdvVrZZEVAqCkzqWJEzt8RuY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CAAAg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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a3f66d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b_mean = b_investor.mean()\n",
    "b_std = b_investor.std()\n",
    "# b交易者收益绘制曲线\n",
    "plt.plot(b_investor)\n",
    "# 水平直线 上线\n",
    "plt.axhline(b_mean + b_std, color='r')\n",
    "# 水平直线 均值期望线\n",
    "plt.axhline(b_mean, color='y')\n",
    "# 水平直线 下线\n",
    "plt.axhline(b_mean - b_std, color='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3  正态分布\n",
    "\n",
    "### 3.3.1 正态分布基础概念"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "股票0 mean均值期望:-0.020\n",
      "股票0 std振幅标准差:1.007\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a4222e8>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Sp46UcFGTRS8rLnpLAAVHGdgALielblP/l5dZIXDyr+tt4pbXGU6CYgZwYsv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\naVPo1lAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a422198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as scs\n",
    "\n",
    "# 均值期望\n",
    "stock_mean = stock_day_change[0].mean()\n",
    "# 标准差\n",
    "stock_std = stock_day_change[0].std()\n",
    "print('股票0 mean均值期望:{:.3f}'.format(stock_mean))\n",
    "print('股票0 std振幅标准差:{:.3f}'.format(stock_std))\n",
    "\n",
    "# 绘制股票0的直方图\n",
    "plt.hist(stock_day_change[0], bins=50, normed=True)\n",
    "\n",
    "# linspace从股票0 最小值－> 最大值生成数据\n",
    "fit_linspace = np.linspace(stock_day_change[0].min(),\n",
    "                           stock_day_change[0].max())\n",
    "\n",
    "# 概率密度函数(PDF，probability density function)\n",
    "# 由均值，方差，来描述曲线，使用scipy.stats.norm.pdf生成拟合曲线\n",
    "pdf = scs.norm(stock_mean, stock_std).pdf(fit_linspace)\n",
    "# plot x, y\n",
    "plt.plot(fit_linspace, pdf, lw=2, c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.0054162 ,  0.00765058,  0.01065132,  0.01461578,  0.0197674 ,\n",
       "        0.02635034,  0.03462041,  0.04483193,  0.05722053,  0.07198226,\n",
       "        0.08925001,  0.10906873,  0.1313716 ,  0.15595953,  0.18248684,\n",
       "        0.21045555,  0.23922051,  0.26800671,  0.29593894,  0.32208298,\n",
       "        0.3454957 ,  0.36528069,  0.38064488,  0.39095113,  0.39576206,\n",
       "        0.39487083,  0.38831588,  0.3763782 ,  0.35956132,  0.33855613,\n",
       "        0.3141938 ,  0.28739138,  0.25909502,  0.2302256 ,  0.20163103,\n",
       "        0.17404851,  0.14807866,  0.12417204,  0.10262765,  0.08360152,\n",
       "        0.06712328,  0.05311796,  0.04143036,  0.0318497 ,  0.02413243,\n",
       "        0.01802213,  0.0132654 ,  0.00962374,  0.00688139,  0.00484974])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.2  实例1：正态分布买入策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-63.3678566  -58.85378699 -45.36941461]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([109, 132,  53])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 保留后50天的随机数据作为策略验证数据\n",
    "keep_days = 50\n",
    "# 统计前454, 切片切出0-454day，view_days = 504\n",
    "stock_day_change_test = stock_day_change[:stock_cnt,\n",
    "                        0:view_days - keep_days]\n",
    "# 打印出前454跌幅最大的三支，总跌幅通过np.sum计算，np.sort对结果排序\n",
    "print(np.sort(np.sum(stock_day_change_test, axis=1))[:3])\n",
    "# 使用np.argsort针对股票跌幅进行排序，返回序号，即符合买入条件的股票序号\n",
    "stock_lower_array = np.argsort(np.sum(stock_day_change_test, axis=1))[:3]\n",
    "# 输符合买入条件的股票序号\n",
    "stock_lower_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_buy_lower(stock_ind):\n",
    "    \"\"\"\n",
    "    :param stock_ind: 股票序号,即在stock_day_change中的位置\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    # 设置一个一行两列的可视化图表\n",
    "    _, axs = plt.subplots(nrows=1, ncols=2, figsize=(16, 5))\n",
    "    # view_days504 - keep_days50 = 454\n",
    "    # 绘制前454天股票走势图，np.cumsum()：序列连续求和\n",
    "    axs[0].plot(np.arange(0, view_days - keep_days),\n",
    "                stock_day_change_test[stock_ind].cumsum())\n",
    "\n",
    "    # [view_days504 - keep_days50 = 454 : view_days504]\n",
    "    # 从第454天开始到504天的股票走势\n",
    "    cs_buy = stock_day_change[stock_ind][\n",
    "             view_days - keep_days:view_days].cumsum()\n",
    "\n",
    "    # 绘制从第454天到504天股票走势图\n",
    "    axs[1].plot(np.arange(view_days - keep_days, view_days), cs_buy)\n",
    "    # 返回从第454天开始到第504天计算盈亏的盈亏序列的最后一个值\n",
    "    return cs_buy[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "买入第 [109 132  53] 支股票，从第454个交易日开始持有盈亏:16.43%\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vd3oprqvB5kREpC2bxYRevwPTS4mOHC8RT+WwGE7hyJgPBoOAwZ5yQ8H5UHJH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a7d2f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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37cpmU25CQJO4vPQ4aloGaOkaIm+FdaqaFZ3N18no28D9wM8MwzgEXPLx+X1q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aae7278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tO8lP0CbtoREvlkwzBbmZtPWM8qkvvcxfvGdf/PlvP+ugKC+TBw7WTPv6hV7D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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11abbb0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 最后输出的盈亏比例\n",
    "profit = 0\n",
    "# 跌幅最大的三支遍历序号\n",
    "for stock_ind in stock_lower_array:\n",
    "    # profit即三支股票从第454天买入开始计算，直到最后一天的盈亏比例\n",
    "    profit += show_buy_lower(stock_ind)\n",
    "\n",
    "# str.format 支持{:.2f}形式保留两位小数\n",
    "print('买入第 {} 支股票，从第454个交易日开始持有盈亏:{:.2f}%'.format(\n",
    "    stock_lower_array, profit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 伯努利分布\n",
    "\n",
    "### 3.4.1  伯努利分布概念"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.2 实例2：如何在交易中获取优势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 设置100个赌徒\n",
    "gamblers = 100\n",
    "\n",
    "def casino(win_rate, win_once=1, loss_once=1, commission=0.01):\n",
    "    \"\"\"\n",
    "        赌场：简单设定每个赌徒一共有1000000一共想在赌场玩10000000次，\n",
    "        但是你要是没钱了也别想玩了\n",
    "        win_rate:   输赢的概率\n",
    "        win_once:   每次赢的钱数\n",
    "        loss_once:  每次输的钱数\n",
    "        commission: 手续费这里简单的设置了0.01 1%\n",
    "    \"\"\"\n",
    "    my_money = 1000000\n",
    "    play_cnt = 10000000\n",
    "    commission = commission\n",
    "    for _ in np.arange(0, play_cnt):\n",
    "        # 使用伯努利分布根据win_rate来获取输赢\n",
    "        w = np.random.binomial(1, win_rate)\n",
    "        if w:\n",
    "            # 赢了 +win_once\n",
    "            my_money += win_once\n",
    "        else:\n",
    "            # 输了 -loss_once\n",
    "            my_money -= loss_once\n",
    "        # 手续费\n",
    "        my_money -= commission\n",
    "        if my_money <= 0:\n",
    "            # 没钱就别玩了，不赊账\n",
    "            break\n",
    "    return my_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 如果有numba使用numba进行加速, 这个加速效果非常明显，不使用numba非常非常非常慢\n",
    "import numba as nb\n",
    "casino = nb.jit(casino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 100个赌徒进场天堂赌场，胜率0.5，赔率1，还没手续费 \n",
    "heaven_moneys = [casino(0.5, commission=0) for _ in\n",
    "                 np.arange(0, gamblers)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 100个赌徒进场开始，胜率0.4，赔率1，没手续费 \n",
    "cheat_moneys = [casino(0.4, commission=0) for _ in\n",
    "                np.arange(0, gamblers)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 100个赌徒进场开始，胜率0.5，赔率1，手续费0.01 \n",
    "commission_moneys = [casino(0.5, commission=0.01) for _ in\n",
    "                     np.arange(0, gamblers)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0JloAAIDWRAsAANCaaAEAAFoTLQAAQGuiBQAAaE20AAAArYkWAACgNdECAAC0JloAAIDW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\ndKgrUa8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ce1c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(heaven_moneys, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AlCRmAACAksQMAABQkpgBAABKEjMAAEBJYgYAAChJzAAAACWJGQAAoCQxAwAAlCRmAACA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\nAACAksQMAABQkpgBAABKEjMAAEBJ/wtngBBR55iccgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa53160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(cheat_moneys, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iBQAAaE20AAAArYkWAACgNdECAAC0JloAAIDWRAsAANDa2jwXqqoTkvx5kgcm+VKSXx9j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\naj1/G8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae36a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(commission_moneys, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAMoQOAAAQBkCBwAAKEPgAAAAZQgcAACgDIEDAACUIXAAAIAyBA4AAFCGwAEAAMoQOAAA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\n+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d3113c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 100个赌徒进场开始，胜率0.5，赔率1.04，手续费0.01\n",
    "moneys = [casino(0.5, commission=0.01, win_once=1.02, loss_once=0.98)\n",
    "          for _ in np.arange(0, gamblers)]\n",
    "\n",
    "_ = plt.hist(moneys, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CAABlCBQAAKAMgQIAAJQxzjfJr7vW2tFJ/jLJiRl+7PGzktw1yZ8muS3JLUme3Hv/amvt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\nEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11abc4d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 100个赌徒进场开始，胜率0.45，赔率1.04，手续费0.01\n",
    "moneys = [casino(0.45, commission=0.01, win_once=1.02, loss_once=0.98)\n",
    "          for _ in np.arange(0, gamblers)]\n",
    "\n",
    "_ = plt.hist(moneys, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
